Artificial neural network implementation on firearm recognition system with respect to ring firing pin impression image - Ahmad Kamaruddin, Saadi; Md Ghani, Nor Azura; Liong, Choong-Yeun; Jemain, Abdul Aziz (Journal Sains dan Matematik UPSI, 2011)
Firearms identification is a vital aim of firearm analysis. The firing pin impression image on a cartridge case from a fired bullet is one of the most significant clues in firearms identification. In this study, a set of data which focused on selected 6 features of firing pin impression images before an entirety of five different pistols of South African made; the Parabellum Vector SPI 9mm model, were used. The numerical features are geometric moments of ring image computed from a total of 747 cartridge case images. Under pattern recognition theory, the supervised features of ring firing pin impression images were then trained and validated using a two-layer backpropagation neural network (BPNN) design with computed hidden layers. A two-layer 6-7-5 connections BPNN of sigmoid/sigmoid transfer functions with ‘trainscg’ algorithm was found to yield the best classification result using cross-validation, where 98% of the images were correctly classified according to the pistols used. Moreover, the network was trained under very small mean-square error (MSE=0.01). This means that neural network method is capable to learn and validate well the numerical features of ring firing pin impression with high precision and fast classification results.